Rising inequality and the implications for the future of private insurance in Canada

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1 Health Economics, Policy and Law (2018), 13, Cambridge University Press This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. doi: /s Rising inequality and the implications for the future of private insurance in Canada MARK STABILE* Professor of Economics and the Stone Chaired Professor in Wealth Inequality, INSEAD, Fontainebleau, France MARIPIER ISABELLE Postdoctoral researcher, James M. and Cathleen D. Stone Centre for the Study of Wealth Inequality, INSEAD, Fontainebleau, France Abstract: Income and wealth inequality have risen in Canada since its low point in the 1980s. Over that same period we have also seen an increase in the amount that Canadians spend on privately financed health care, both directly and through private health insurance. This paper will explore the relationship between these two trends using both comparative data across jurisdictions and household-level data within Canada. The starting hypothesis is that the greater the level of inequality the more difficult it becomes for publicly provided insurance to satisfy the median voter. Thus, we should expect increased pressure to access privately financed alternatives as inequality increases. In the light of these implications, the paper considers the implications for the future of private insurance in Canada. Submitted 1 April 2017; revised 19 May 2017; accepted 1 July 2017; first published online 25 January Introduction The share of earned income held by the top 1% of Canadians has doubled since the late 1970s from 8% to 16%. By 2008, top income earners were taking home a bigger share of national income than at any point since the 1940s (see Figure 1). The extent to which this growth in inequality should be of concern to citizens depends on how the concentration of income among a small share of the population affects several dimensions of well-being. The list of potential areas of concern is significant: researchers have explored the connection between income inequality (as separate from poverty) on economic growth, social mobility, crime and social cohesion among other areas (Atkinson, 2016; Wilkinson and Pickett, 2009; Stiglitz, 2012). One channel through which inequality, and in particular income concentration among the top earners, might harm public institutions is by weakening support for public programs or the financing of public programs for which alternatives *Correspondence to: Mark Stabile, Professor of Economics and the Stone Chaired Professor in Wealth Inequality, INSEAD Fontainebleau, France. mark.stabile@insead.edu 406

2 Rising inequality and the implications for the future 407 Figure 1. Evolution of income shares held by top income fractiles Source: World Wealth and Income Database. Note: Author s calculations from World Wealth and Income Database plus Statistics Canada. or supplements can be found in private markets. Public programs which aim to provide a universal benefit to a heterogeneous population often target (implicitly or explicitly) the median voter. 1 That is, they provide a level of benefit thatmaynot reflect the desired benefit level for any single voter or group of voters, but rather aim for a level of benefit that is close enough to what most voters desire such that it acceptable to many. Publicly financed health programs such as Canadian Medicare would be an example of such a benefit a universal program funded through general tax revenues that aims to provide a level and quality of service that is acceptable to a majority, but that given budgetary realities, must make trade-offs in terms of the quantity and quality of services funded. Publicly financed health care in Canada has historically managed this trade-off reasonably well and continues to receive high (albeit time varying) levels of support by most Canadians. As the distance between the incomes of the top income earners in Canada and the median income grows, any benefit that targets the preferences of the latter may move further away from the desired level of benefits for the top group. Preferences for the level and quality of health care desired are likely to increase with income. Mechanically, an increase in income and wealth concentration among top earners will then lead to a desired level of health care services that is ever distant from what publicly financed health care provides. This paper aims to explore whether there is evidence of such a relationship based on the changes in concentration of income at the top of the distribution and on the evolution of expenditures on privately financed health care services both across the Organization for Economic Co-operation and Development (OECD) and, in particular, in Canada. It builds on a considerable literature (partially reviewed below) looking at the effects of 1 And, in some cases, to pivotal voters whose income will be below the median (Epple and Romano, 1996b).

3 408 MARK STABILE AND MARIPIER ISABELLE changes in the income distribution on the provision and financing of public goods, but instead focuses on the effects of changes in the income distribution on the use of private services. It is worth noting that preferences to use more or higher quality health care do not need to translate into people actually receiving better quality care. Indeed, there is considerable research suggesting that more care is not always better care and can often end up causing harm (Welch, 2015). For the purposes of this analysis, it is sufficient that individuals choose to seek private alternatives to publicly financed health care, signaling a departure between their preferences and the care provided by the publicly financed system. We explore whether there is evidence of such a relationship by looking at the within-country correlation between top income shares and spending on private care and private insurance using panel data from OECD countries over a 35-year period. We then look specifically within Canada at the relationship between changes in income concentration and spending on both private health care and private insurance using household expenditure data. Canada s public system allows for individuals to insure privately for services not covered under the public system, and has also come under increasing pressure recently to allow individuals to spend privately on otherwise publicly available services. The potential effects of changes in the income distribution could come through either of these two channels. Our country-level findings suggest that private health expenditure increases with lagged increases in the top 1% share as does spending on private health insurance. We find somewhat weaker evidence of a relationship between lagged top income shares and out-of-pocket health care costs. Within Canada we find evidence of increases over time in the relative spending of top income earners on health care and private health insurance controlling for the direct income-health care spending relationship as well as general changes in private health care spending over time. That is, there is a fanning out of the relationship between private health spending of those in the top income decile relative to the rest of the population over time. Both these findings suggest that there may be reason for concern about the relationship between growing income concentration and the ability of publicly financed health care to provide a universally acceptable benefit. They also suggest that continued growth in income concentration at the top of the distribution may result in a greater demand for private health spending and insurance. 2. Previous literature Combining existing estimates of the income elasticity of health expenditure with the insights from the literature on inequality and social spending raises questions on the relationship between trends in inequality and the use of privately financed health care. First, and fuelled by recent trends in developed countries suggesting that aggregate income growth has been paralleled by marked increases in health expenditure (both in levels and as a share of GDP), research has focused on uncovering the

4 Rising inequality and the implications for the future 409 income elasticity of health expenditure. Although the range of estimates in household-level and country-level empirical analyses is quite wide, the evidence points towards positive income elasticities (e.g. Acemoglu et al., 2013 for the United States and Di Matteo and Di Matteo, 1998 for Canada), some work even presenting health care as a potential superior/luxury good (Hall and Jones, 2007; Li et al., 2016). 2 Such positive income elasticities of health expenditure, especially at the household level, suggest that other factors held constant, inequality coming from income growth concentrated among high income earners may disproportionately raise the demand for health-related goods at the top of the income distribution. The demand for health care at the bottom of the distribution may remain relatively unchanged as inequality rises if middle- and low-income earners wages stagnate, and even if they slightly decline (Culyer, 1988). However, important work on the determination of publicly provided goods and services through democratic processes suggest that the preferences of high income earners may remain unmet by the tax-financed programs receiving enough popular support to be implemented. Indeed, majority voting models have been suggested in which the level and nature of publicly provided goods and services (including health care) and the corresponding tax rate are influenced by the shape of the income distribution. If preferences differ between income levels, theoretical work suggests that (i) a two-tier (Epple and Romano, 1996a; Lülfesmann and Myers, 2011) or dual-provision health care system (Epple and Romano, 1996b) will likely emerge as a stable policy choice and (ii) the scope of the chosen program or its level of care will meet the preferences of a median voter whose income will be close to (in the case of progressive taxes) or below (in the case of linear taxes) the median income. A common thread in these models is that if health care is a normal (or superior/luxury), the preferences of top income earners will exceed the level of publicly provided care and this, even if the equilibrium reached is stable. While substantive attention has been given to understand the impact of changes in the income distribution on populations choices of tax rates and levels of publicly funded services, 3 our aim in this paper is different. We do not seek to test how top income earners choose their preferred tax rates or their preferred level of 2 Hall and Jones (2007) present a multi-period model in which investing in health care earlier in life can be optimal for the rich, as it allows them to extend their life expectancy and to smooth consumption of nonhealth-related goods and services over a longer lifespan. This would help them avoid the diminishing marginal returns that would come from concentrating consumption over a shorter lifespan. Their theoretical framework allows for an income elasticity superior to the unity, something they say would be hard to measure empirically, among other reasons because of the availability of health insurance. Li et al. (2016) estimate an elasticity of private health expenditure greater than unity using a model in which the mix of public and private finance in health care is endogenously determined, and that they calibrate to the Canadian economy. 3 Lupu and Pontusson (2011) find a positive association between the skew of the income distribution and governments level of social expenditures across OECD countries. Corcoran and Evans (2010) also find a positive association between inequality and local expenditures on public education across US school districts. Bénabou (1996), Kenworthy and Pontusson (2005), however, point to mixed results especially for cross-country analysis.

5 410 MARK STABILE AND MARIPIER ISABELLE publicly provided health care, but rather whether and how they respond through the use of privately financed care when the public health care system moves further away from their own preferences. 3. Country-level analysis We first empirically investigate the potential relationship within countries over time between income concentration at the top of the distribution and changes in the role of private finance in health expenditures and health insurance. To do so, we focus on a group of OECD countries which we observe between 1980 to 2015, a period over which the degree of inequality has varied substantially in many developed countries. While such an analysis does not allow for an in-depth investigation of the impact of growing income concentration on households demand for health care and private health insurance, it provides an opportunity to uncover broader patterns, which we further unpack by looking at individual spending data in Section Data We use country-level data from the OECD (OECD, 2016), the World Bank (WORLD BANK, 2016) and the World Wealth and Income Database (WID) (Alvaredo et al., 2016). We restrict our analysis to a set of 20 countries for which some consistent and comparable data on income inequality and health care expenditure is available: Australia, Canada, Colombia, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Korea, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom and the United States. 4 These countries are characterized by substantial heterogeneity in terms of health care systems, including in the structure of their private health insurance markets. While such differences likely affect how changing inequality will influence private health spending, our main empirical analysis focuses on changes in income concentration within countries over time. Nevertheless, we note that the patterns emerging from the analysis conducted in this section represent an average estimated relationship across the systems represented in the sample Inequality measures Our main measure of inequality is the annual income share going to the top 1% income earners in each country from the World Wealth and Income Database. 4 Although the main variable of interest are available for most years for the countries forming our final sample, country-specific gaps in the coverage of some variables result in our panel not being balanced. Moreover, previous research (e.g. Lupu and Pontusson, 2011) has identified the United States to be an outlier in terms of health care expenditure and financing; our results are generally qualitatively robust to excluding the United States from our sample of countries.

6 Rising inequality and the implications for the future 411 This measure has been at the forefront of the public debate on rising inequality in recent years, as it provides information on inequality at the top of the income distribution. This is a force we hypothesize is driving pressures to increase access to privately financed health care. 5 Our analysis focuses on a definition of income that excludes capital gains given data availability constraints. However, data from a few countries for which top income shares are available both for income excluding and including capital gains suggests that the trends do not vary substantially across income definitions. Although top income shares are available for most combinations of countries and years in our sample, there are some gaps in the coverage of our panel of 554 country-year observations. Notably, income shares are available for the United States until 2015, but for most countries the coverage ends between 2010 and 2012, Portugal being the exception with data on top income shares available until 2005 only. As top income shares do not provide information on inequality within the whole income distribution, we also test the robustness of our results by considering alternative measures of inequality. First, we obtain Gini coefficients from the OECD income distribution database. We focus on the Gini coefficient after taxes and transfers which are systematically lower than market Gini coefficient. Availability for this variable is limited to 238 of the country-year combinations for which information is available on top income shares. This reduced sample includes 19 countries (Gini coefficients are not available for Colombia), over 35 years. The countries for which the most observations are available are the United States (35 observations) and Canada (29 observations), and the countries with the smallest number of observations are Portugal (2) Japan (6) and Australia (6). We also obtain the ratio of income levels corresponding to the thresholds identifying the 90th and the 50th percentiles in the income distribution, and a similar ratio for the 50th and the 10th percentiles. Those ratios provide a more direct comparison between the levels of income achieved at the top and at the is the upper bound value of income of the 90th percentile, y 50 ct is the median level of income and y 10 ct is the upper bound value of income of the 10th percentile in the income in country c and year t, they correspond to: bottom of the distribution 6 :Ify 90 ct Inequality top ct = y 90 ct =y50 ct : Inequality bottom ct = y 50 ct =y10 ct : The ratios are available for 185 country-year pairs for which top income shares are available. Given the availability of other control variables, 143 of these observations 5 Although most of the results we report employ the top 1% income shares, the income shares of the top 5% and the top 10% have followed relatively similar trends over the past 35 years. 6 Lupu and Pontusson (2011) suggest combining the two ratios to form a measure of the skew of the income distribution: skew ct = ½y 90 ct =y50 ct Š=½y50 ct =y10 ct Š.

7 412 MARK STABILE AND MARIPIER ISABELLE are used in the empirical analysis [corresponding to 19 countries between 1982 and 2014, observed between 2 (Portugal) and 19 (Canada) times each] Privately financed health care Our measures of health expenditure are taken from the OECD health expenditure and financing data set. 7 In addition to the total health expenditure (from all sources), we obtain the level of private and public (governmental) health expenditure at the country-year level, as well as the proportion of all health expenditure financed privately. These variables are available for the full set of observations within the sample for which the top 1% income shares are available. We also retrieve the share of the population covered (exclusively or partially) by private health insurance plans from the OECD Social Protection data set. 8 Finally, we obtain country-level information on out-of-pocket health expenditure (as a share of total health expenditure) from the World Health Organization global health expenditure database. 9 Coverage for those two last variables is limited to subsamples of country-year observations, as described in Section Other controls variables We obtain information on a series of variables, which likely influence overall patterns on health expenditure, and more importantly on private health expenditure, which we include as control variables. We turn to the demographic and economic reference data from the OECD to obtain, for each observation in our sample, information on population counts, the share of the population aged 65 and older, the civilian employment rate and public health expenditure. We obtain a measure of GDP and the level of total government expenditure per country and year from the World Bank national accounts data, and the OECD national accounts data files. Finally, we retrieve the average income per tax unit from the World Wealth and Income Database Summary statistics We define our main sample as the subset of country-year pairs for which the top 1% income share and the control variables described in the section above are 7 From data assembled by the OECD, EUROSTAT and the WHO Health Accounts SHA Questionnaires. 8 Not all countries in the sample detail the types of private insurance that individuals are covered by, We therefore use the share of the population covered by some form of private insurance, a measure reported by the OECD. 9 Available from the World Bank s database. Out-of-pocket health expenditure as a share of total private health expenditure is also available. Total private health expenditure is also available from the World Bank, but the values and availability are similar to the data obtained from the OECD (with the exception of a few observations for Germany, due to a policy change that was captured differentially by the OECD and the World Bank given their definition of private health expenditure). Our results are robust to using either data source.

8 Rising inequality and the implications for the future 413 available, corresponding to 453 observations. Although information on total, public and private health expenditure is available for each of these country-year pairs, the subsample for which the share of total health expenditure financed outof-pocket is available is limited to 265 observations between 1995 and The number of observations is reduced to 135 (14 countries, excluding Finland, Japan, Italy, the Netherlands, Norway and Sweden) when considering the share of the population with private health insurance. Table 1 summarizes the descriptive statistics for the main estimating sample. On average, the share of total national income concentrated among the top 1% of income earners is 8.5%, although it varies between a little less than 4% in Sweden in 1981 and more than 18% in the United States in the second half of the 2000s. The share also reaches a little more than 20% in Colombia in Across countries, the average share of income held by the top 1% increases substantially over the years, from levels around 6% in the early 1980s to highs around 9 10% from the mid-2000s onwards. Although specific shares vary across countries, the upward trend over the past three decades is observed in most countries in the sample. The Gini coefficient generally increases within countries through time, but its distribution is more compressed, with an average of 0.3, a minimum of 0.2 (Sweden 1983 and 1991) and a maximum of 0.4 (United States in 2013 and 2014). On average, the ratio of the upper bound in disposable income of the 50th and the 10th percentile of the income distribution is greater than the same ratio for the upper bound in disposable income of the 90th to the 50th percentile, respectively, of 2.10 and 1.90, but the evolution of these ratios through time varies across countries. Inequality at the top of the distribution is on average lowest in Denmark, Norway, Finland and Sweden, while it is higher in New Zealand, the United Kingdom and the United States. At the bottom of the distribution, lower levels of inequality in the sample are found in Sweden and the Netherlands, while higher levels are found in the United States. On average, 27% of all health expenditure is privately financed. In countries for which information on out-of-pocket expenditure is available, it represents on average 18% of health expenditure. Private health insurance coverage rates average 37% across our sample, however, the share of the population with at least partial private health insurance coverage is highly variable across countries in our sample, from as little as 0.80% of the population in Denmark in 2001 to 91.6% of the population in France in Although coverage rates vary through time within countries, stark differences are in general observed across countries not within them. Important differences exist within and across countries for some other variables presented in Table 1 and are likely to influence private health expenditure, such as the employment rate, total government expenditure, gross domestic product and 10 The high proportion of privately insured in France corresponds to complementary plans, covering the difference between the total cost of care/medicine and the proportion covered by the public insurer.

9 414 MARK STABILE AND MARIPIER ISABELLE Table 1. Summary statistics, cross-country analysis Mean SD Min Max Observations Top 1% income share Gini coefficient (after taxes and transfers) Disposable income ratio: 90th to 10th percentile Disposable income ratio: 90th to 50th percentile Disposable income ratio: 50th to 10th percentile Skew Ratio: private to public health expenditure Private health expenditure (% all health expenditure) Population with private insurance (% total population) Out-of-pocket health expenditure (% all health expenditure) Total population (/100k) Proportion of the population aged Employment rate Average income (per tax unit) Ln Government expenditure Ln GDP GDP = gross domestic product. Note: All income and expenditure variables are expressed in U.S. dollars of average income per tax unit. Noticeable upward trends in the share of the population aged 65 or more, a segment of the population more likely to be heavy users of health care although more likely to be covered by public health insurance plans in many countries, also potentially plays an important role. All these variables will be accounted for in our main empirical analysis. 3.2 Results We empirically investigate the relationship between the income share accruing to the top 1% of the income distribution and each of our measures of the role of private finance in health care with the following specification: Ln Private ct = α + β Ln Inequality c;t k + γ Ln Public ct + X 0 ct + ϕ c + μ t + ε ct ; (1) where Ln Private ct corresponds to the natural logarithm of the level of private expenditure in country c and year t and Ln Public ct is the natural logarithm for public health expenditure, so equation (1) corresponds to the logged version of a regression in which the dependent variable would be the ratio of private to public health expenditures. The main independent variable, Ln Inequality, is the share of income held by the top 1% and X ct is a vector of the natural logarithm of the controls variables enumerated in Section 3.1. A full set of year fixed effects, µ t, controls for shocks that might simultaneously affect all countries in the sample.

10 Rising inequality and the implications for the future 415 Table 2. Ln income share of top 1% on Ln private health expenditure (1) (2) (3) Top 1% [t] Top 1% [t 1] Top 1% [t 2] Ln Top 1% income share (0.142)*** (0.155)*** (0.143)*** Ln Public health expenditure (0.129)*** (0.161)*** (0.141)*** Ln GDP (0.304)*** (0.386)** (0.370)** Ln Government expenditure (0.230)*** (0.276)*** (0.269)*** Ln Population (/100k) (0.456) (0.676) (0.650) Ln Population aged > (0.190)*** (0.235)*** (0.239)*** Ln Employment rate (0.344) (0.399) (0.354) Ln Average income (per tax unit) (0.183)** (0.204)* (0.204)* Country FE X X X Year FE X X X Observations R GDP = gross domestic product. Notes: Robust standard errors in parantheses. Income shares from the World Wealth and Income Database (Alvaredo et al., 2016). GDP, government expenditure, private and public health expenditures, population and employment rate from the OECD (OECD, 2016). All values in U.S. dollars of Unbalanced panel, data available from 1982 to ***p < 0.01, **p < 0.05, *p < 0.1. Finally, we include country fixed effects, ϕ c, to control for unobserved timeinvariant features of country health care systems that are likely to have an impact on the prevalence of private health care financing. Given the addition of these fixed effects, our estimate for β can be interpreted as the within-country relationship between changes in inequality and changes in private health care expenditure. We finally allow our inequality measure to enter with a zero-totwo-year lag over the full sample period. The results from this specification are summarized in Table 2. The coefficients reported in the first column of Table 2 income share suggests a positive and statistically significant correlation (0.778) with the contemporaneous income share of the top 1% income earners. This positive and statistically significant elasticity persists as we turn to one- and two-year lagged values of the top 1% income share, although the magnitude of the coefficient decreases with the lags, to 0.65 and 0.61, respectively, suggesting private health expenditure could react relatively quickly to changes in the level of inequality. As expected, private health expenditure is negatively associated with the share of the population aged 65 and older, potentially driven by the fact that public health coverage is often more generous for people in that age range. The association between private health expenditure and the average income per tax unit is also positive, as is the association with national GDP. The association between the natural logarithms of private and public expenditure is negative, and statistically significant, which is consistent with the crowd-out effects found in Flood et al. (2004).

11 416 MARK STABILE AND MARIPIER ISABELLE Table 3. Ln income share of top 1% on Ln population with private health insurance (1) (2) (3) Top 1% [t] Top 1% [t 1] Top 1% [t 2] Ln Top 1% income share (0.339)** (0.320)** (0.313)*** Ln Public health expenditure (0.686) (0.707)* (0.708) Ln GDP (1.247)** (1.424)* (1.457) Ln Government expenditure (0.765)** (0.850)* (0.804)* Ln Population aged > (1.250)* (1.217)** (1.239)*** Ln Employment rate (0.908) (1.010) (0.961) Ln Average income (per tax unit) (0.788) (0.855) (0.832) Country fixed effects X X X Year fixed effects X X X Observations R GDP = gross domestic product. Notes: Robust standard errors in parantheses. Income shares from the World Wealth and Income Database (Alvaredo et al., 2016). GDP, government expenditure, private health insurance, population and employment rate from the OECD (OECD, 2016). All values in U.S. dollars of Unbalanced panel, data available from 1995 to ***p < 0.01, **p < 0.05, *p < 0.1. We slightly alter the specification described above and replace the control for the natural logarithm of public health expenditure with the natural logarithm of total health expenditure, to effectively estimate the logarithmic version of a regression of our measure of inequality on the private share of total health expenditure (not shown). Overall, we obtain coefficients on the top 1% income share varying between 0.26 and Similar patterns as those described in Table 2, including smaller coefficients on the inequality variable as we move towards longer lags, can be observed. Individuals (and therefore countries) can increase private expenditure on health care in a variety of ways including: (i) an increase in the scope or quality of the voluntary health insurance schemes purchased by individuals already covered by some form of private insurance; (ii) an increase in the share of the population buying private health insurance coverage (purchased individually, or provided through an employer-sponsored plan); or (iii) an increase in the total amount spent out-of-pocket for health care services and medication. Tables 3 and 4 present results that speak to the last two of these channels. The estimates suggest that the uptake in private health insurance in the population is likely the main driver behind the positive association between inequality and private health care spending. Table 3 presents the results when we estimate a version of equation (1) in which Ln Private ct represents the natural logarithm of the population covered by private health insurance, which corresponds to estimating the logged version

12 Rising inequality and the implications for the future 417 Table 4. Ln income share of top 1% on Ln out-of-pocket health expenditure (1) (2) (3) Top 1% [t] Top 1% [t 1] Top 1% [t 2] Ln Top 1% income share (0.105)* (0.085)* (0.084)** Ln public health expenditure (0.127) (0.127) (0.121) Ln GDP (0.256)** (0.253)** (0.249)* Ln Government expenditure (0.179)*** (0.174)*** (0.173)** Ln Population (/100k) (0.529) (0.494) (0.489) Ln Population aged > (0.169) (0.170) (0.169) Ln Employment rate (0.194)** (0.179)*** (0.179)*** Ln Average income (per tax unit) (0.147)*** (0.143)*** (0.141)*** Country fixed effects X X X Year fixed effects X X X Observations R GDP = gross domestic product. Notes: Robust standard errors in parantheses. Income shares from the World Wealth and Income Database (Alvaredo et al., 2016). GDP, government expenditure, private health expenditure, population and employment rate from the OECD (OECD, 2016). Out-of-pocket health expenditure from the World Bank (WORLD BANK, 2016). All values in U.S. dollars of Unbalanced panel, data available from 1995 to ***p < 0.01, **p < 0.05, *p < 0.1. of a regression of the private health insurance coverage in the population on the income share of the top 1%. 11 We estimate a positive and statistically significant relationship (0.86) between the top 1% income and the share of the population covered by some form of private health insurance. These results are consistent when controlling for the log of public health expenditure or for the log of total health expenditure. Re-estimating the same model separately for country-year pairs with a private insurance coverage below and above 32.5% (the median coverage rate) suggests that this estimated impact mostly comes from contexts where baseline private coverage is low. We also note that, in our sample, the income share of individuals between the 90th and the 99th percentile of the income distribution has increased as the income share of the top 1% was also increasing. Our result might therefore capture the fact that as individuals at the top of the income distribution (described more broadly than solely the top 1%) get relatively richer, their preferences for faster access to care, improved quality of care (either through the quality of the care itself, or through a more extensive set of amenities in health care establishments), also leads them to opt in private health insurance schemes. Substituting the natural logarithm of the top 1% income share by that of the top 5% and top 10% income share in a similar specification, we indeed estimate a positive relationship between 11 Ln Population ct in included as a control variable.

13 418 MARK STABILE AND MARIPIER ISABELLE inequality at the top of the income distribution and private health insurance coverage. Table 4 summarizes the results obtained when the dependent variable in equation (1) is defined as the natural logarithm of out-of-pocket health expenditure (controlling for the natural logarithm of public health expenditure). We estimate a weaker relationship, suggesting that the growth in privately financed health expenditure observed as inequality increases is likely due to an increase in private insurance premiums, rather than in co-payments or in out-of-pocket payments for services that are not covered by public or private plans. Appendix Tables A1 A4 (online Supplementary material) suggest that our results are generally robust to measuring inequality using Gini coefficients in lieu of top income shares. However, statistically significant associations are harder to capture using disposable income ratios to consider separately the distance between the median and, respectively, the top and the bottom of the income distribution. Our cross-country estimates are suggestive of a relationship between top income shares and private health spending, particularly through the purchase of private insurance. However, this finding could reflect a variety of underlying mechanisms. To try and understand the extent to which the concentration of income at the top end of the distribution is driving this relationship we turn to a micro-data analysis of health care spending by Canadian households. 4. Within-country analysis: the Canadian case The Canadian context offers an interesting environment in which to explore the hypothesis that increasing income concentration (as distinct from absolute income) may increase the use of private health care services. Figure 2 presents the evolution of aggregate health expenditure and inequality as measured by top income share since the 1980s. While most physician and hospital services are covered by universal public insurance, Medicare exists alongside a private market for many health professionals services, prescription drugs, long-term care, dental care as well as some physician services. 4.1 Data We obtain information on income and spending on health-related goods and services at the household level from the public use micro-data files of the Survey of Household Spending (SHS), an annual survey conducted and administered by Statistics Canada (Income Statistics Division Statistics Canada, 2009). The SHS collects information on Canadians spending patterns, with the exclusion of those who are institutionalized (including those living in nursing homes), who live in military camps or who live on Indian reserves. Our main estimating sample is composed of thirteen cross-sectional waves of the survey, covering the period

14 Rising inequality and the implications for the future 419 Figure 2. Evolution of health expenditures and top income shares. (a) Health expenditure (share of GDP) in Canada and top income shares. (b) Health expenditure per capita in Canada and top income shares Source: Health expenditures from OECD, income shares from World Wealth and Income Database. spanning from 1997 to In addition to providing detailed information on households sources of income before and after taxes and transfers, the SHS provides a granular overview of their annual expenditures on various categories of goods and services, including but not limited to shelter, clothing, transportation and most importantly for the purposes of this analysis health care. In each 12 Certain variables were originally coded differently in the 1997 wave of the SHS. We recoded all relevant variables to ensure a consistent definition over our sample.

15 420 MARK STABILE AND MARIPIER ISABELLE wave of the survey, information on expenditure and income was collected through interviews and recall bias was addressed with procedures including the verification of respondents answers using households receipts. All expenditures and income values are transformed in constant 2002 dollars using the all-items consumer price index published by Statistics Canada Household income and income fractiles We focus on total income at the household level, consisting of earnings, as well as income from investment and other sources, including transfers but before taxes. To assign a position in the income distribution to each household in the sample, we first use the survey weights to generate a distribution of household income for each year in our data, and identify the annual income thresholds corresponding to each percentile. To ensure that this procedure generates thresholds that are representative to the true distributions, we create similar thresholds for individuals (instead of households) in the SHS, and compare them with the thresholds derived from individual Canadian tax filers data in the Longitudinal Administrative Database. 14 The threshold values for the top 10% of income earners and for the median income earner are quite similar across sources. The SHS estimates of income thresholds for the top 1% are, however, less precisely estimated and our empirical approach, therefore, will mostly focus on the spending patterns of households in the top 10%. We allocate all other households to one of the three following income fractiles: 51st to 90th percentiles, 21st to 50th percentiles and the bottom 20 percentiles Household health care expenditures We consider two broad measures of overall household health care expenditure. First, total health care expenditure corresponds to the sum of 11 categories capturing various dimensions of health care spending by households: hospital and other residential facilities, physician care, other health care professional services, other health care and medical services, prescription drugs, other medicinal or pharmaceutical products, private health insurance, public hospital or medical or drug plans, health care supplies, eye care and dental care. A detailed description of the items covered by each category is given in Table A5 in the online Appendix. Second, direct health care costs to the household corresponds to expenditures from total health care costs, from which private health insurance and public hospital or medical or drug plans are subtracted. In Section 4.2, we look more 13 Statistics Canada, CANSIM, table , accessed 13/10/ Threshold estimates from the Longitudinal Administrative Database (LAD) are available from CANSIM table Comparisons are made using market income since transfers are only available at the household level in the SHS. We benchmark our income thresholds using individual incomes given that LAD estimates are not available for household income thresholds, as shown in Figure A1 in the online Appendix. The evolution of the income thresholds identifying each fractile, and of the mean income per fractile, are depicted in Figure A2.

16 Rising inequality and the implications for the future 421 closely at four individual categories of expenditures: prescription drugs, private insurance, hospital and other residential facilities and physician care. Looking separately at these categories helps understand if private health spending is focused on accessing care that replaces or complements the features of the Canadian universal health system Household characteristics We obtain a series of socio-demographic information from the SHS to control for household-specific characteristics in our empirical analysis. In addition to household size, we use information of the number of individuals aged 65 and above, the number of children, the age of the youngest child (0 5 yearsoldand6 18 years old), the marital status of the main respondent, as well as the household s total annual expenditure, from which we can derive a measure of annual non-health expenditure Summary statistics Our main estimating sample is composed of all observations coming from the 10 Canadian provinces between 1997 and 2009, excluding those for which information on household composition is missing. We further exclude all households reporting a negative total income (from earnings, investment, other sources and transfers, before taxes) or a negative amount for total annual expenditures (excluding taxes, non-health insurance payments and contributions, and gifts). Our final sample consists of 186,577 households. Summary statistics for our sample are shown in Table 5. Average annual household spending is $ on private health insurance, $ on prescription drugs and $ on dental care. Consistent with the nature of the Canadian health care system and with the provision of the Canada Health Act, average expenditures on physician care and hospitals are low, at respectively, $18.22 and $18.06 per year. 15 Table 6 takes a closer look at characteristics and health expenditure patterns for households in different income fractiles. High and low income households in our sample are significantly different. Households in the bottom 20% are more likely to be composed of only one individual, and are on average less than half the size of households in the top 10%, who are more than four times more likely to have a married respondent. Households with an income below the median are also more likely to include at least one individual aged 65 or more, and are less likely to include children. There is a substantial difference in the average income between households in the bottom 20%, at $14,279, and those in the top 10%, $180,233. Total expenditures also increase through the income distribution, although the 15 Expenditure on hospital care include all charges on hospital bills, including phone and television charges, room upgrades and access to additional amenities within inpatient facilities/nursing homes/residential care facilities. Expenditure on physician services include all out-of-pocket payments to physician whose services are sought in private clinics or fees paid to clinics for physician services that are not covered by Medicare (e.g. phone consultations, etc.).

17 422 MARK STABILE AND MARIPIER ISABELLE Table 5. Summary statistics, survey of household spending, Canada Mean SD Min Max Household characteristics Household size Number of seniors Married couple At least one child (<18 years) at home Youngest child 0 5 years Total income 60,820 59, ,927,880 Total consumption expenditure 42,248 27, ,763 Health expenditure Total health care ,765 Direct health care costs ,765 Health care supplies ,271 Prescription drugs ,879 Other medicines and pharmaceutical products ,112 Physician care ,000 Eye care goods and services ,051 Dental care ,580 Hospitals ,947 Non-physician health practitioner services ,239 Other medical services ,293 Public hospital, medical and drug plans ,147 Private health insurance plans ,497 Observations (weighted) 158,573,745 Observations (unweighted) 186,577 gradient is less pronounced than the increase in income between the bottom, middle and top fractiles. As expected, health expenditure increases as one moves from low- to highincome households. The bottom panel of Table 6 suggests that the health expenditure-income gradient would become more pronounced at the top of the income distribution; for example, households in the top 1% spend nearly 1.5 times what household located between the 90th and the 99th percentiles spend on health-related goods and services than households. 16 A similar pattern is observed for almost all health expenditure categories, with the exception of prescription drugs. Households in the bottom 20% of the income distribution spend less on prescription drugs, which could be explained both by constrained financial resources and possible eligibility for public drug plans for low income families. Out-of-pocket expenditure on prescription drugs is higher for the rest of households in the bottom half of the income distribution, but mostly decreases for households in the upper half income distribution. 16 The average annual health expenditure for households in the top 10%, excluding households in the top 1% is $2553 in our sample.

18 Rising inequality and the implications for the future 423 Table 6. Summary statistics by income fractile, survey of household spending, Canada Bottom 20% 21st to 50th pctile 51st to 90th pctile Top 10% Top 1% Household characteristics Household size Number of seniors Married couple Household with youngest child 6 18 years Household with youngest child 0 5 years Total income 14,279 35,373 76, , ,529 Total consumption expenditure 17,788 31,117 52,613 88, ,315 Household health expenditure Health care Direct health care costs Health care supplies Prescription drugs Other medicines and pharmaceutical products Physician care Eye care Dental care Hospitals Non-physician health practitioners services Other medical services Public hospital, medical and drug plans Private health insurance plans Observations (weighted) 32,643,098 47,078,620 64,749,203 14,102,824 1,473,180 Observations (unweighted) 42,207 59,594 71,710 13, pctile = percentile. 4.2 Results Our main empirical specification is given by equation (2), in which Health expenditure ipt corresponds to the expenditure of household i residing in province p observed in year t. Income Fractile ipt consists of a series of three dummies indicating the household s position in the income distribution, either between the 21st and the 50th percentiles, between the 51st and the 90th percentiles, or in the top 10% (the omitted category being the bottom 20% of the distribution). Other control variables included in X ipt are total household income, 17 the number of adults aged 65 and above within the household, the presence of children in the household, the total number of individuals within the household and the marital status of the main respondent to the SHS. We also include a vector of province fixed effects, ϕ p to account for the fact that, beyond the main principles set in the Canada Health Act, provinces can decide on the nature and scope of additional publicly provided health coverage, and have jurisdiction on the organization of the health care system. Consequently, prescription drugs for some population groups or services offered by certain health professionals, for example, vary across jurisdictions within the country. In this context, the inclusion of province fixed 17 The results robust to controlling for after tax income rather than total income.